Research Article
LIS Program
Representatives’ Perspectives on Preparing Students for Careers in Research
Data Management and Data-Related Librarianship
Jennifer Abel
Research Data Management
Librarian
Libraries and Cultural
Resources
University of Calgary
Calgary, Alberta, Canada
Email: jennifer.abel@ucalgary.ca
Alisa B. Rod
Research Data Management
Specialist
McGill University Libraries
Montréal, Québec, Canada
Email: alisa.rod@mcgill.ca
Received: 12 Sept. 2024 Accepted: 9 Dec. 2024
2025 Abel and Rod. This is an Open
Access article distributed under the terms of the Creative Commons‐Attribution‐Noncommercial‐Share Alike License 4.0
International (http://creativecommons.org/licenses/by-nc-sa/4.0/),
which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly attributed, not used for commercial
purposes, and, if transformed, the resulting work is redistributed under the
same or similar license to this one.
DOI: 10.18438/eblip30622
Abstract
Objective
– This study aims to contribute a qualitative
analysis of the perspectives of LIS program representatives on providing
research data management (RDM) and data librarianship training opportunities to
their students. The primary objectives of the study are to determine which
programs currently provide training opportunities for students in RDM and
related areas, as well as whether programs have provided such opportunities in
the past and/or intend to do so in the future.
Methods – This study
incorporates in-depth qualitative empirical evidence in the form of five
semi-structured interviews of representatives of Canadian LIS programs to investigate
first-hand perspectives on the RDM and data-related opportunities they can
provide to their students.
Results
– The interviews identified five major themes
related to LIS programs’ RDM and data-related training offerings, including the
range of formal and informal opportunities currently available in the programs;
the ways in which the representatives would mentor and advise students
interested in RDM or related career paths; the challenges posed by both the
lack of instructors for RDM and data-related courses, and the lack of students
who are interested in, or ready to pursue, data-related careers; the need for
programs to develop a curriculum that meets the requirements of many
stakeholders; and the effects of the rapidly changing library landscape on LIS
curriculum development.
Conclusion – This qualitative study sheds light on both the support that Canadian
LIS programs can provide to students who are interested in RDM and data-related
careers in academic libraries, and the challenges those programs face in
providing that support.
Introduction
The need for
academic librarians to develop skills and competencies in research data management
(RDM) and data services has been recognized for well over a decade (Andrikopoulou et al., 2022; Corrall,
2012; Corrall et al., 2013; Koltay,
2016; Perrier et al., 2018; Pinfield et al., 2014; Tenopir et al., 2015). This need is set to grow with the
implementation of data management, deposit, and/or sharing policies such as the
Tri-Agency RDM Policy in Canada (Government of Canada, 2021) and the NIH Data
Management and Sharing Policy in the United States (National Institutes of
Health, 2020), which will further increase both researchers’ obligations
related to properly managing their research data and the support they require
to do so (Khair et al., 2020; Steeleworthy,
2014). However, it has also been observed that many practicing
librarians—primarily those who are not already supporting research data
services as part of their job, but even some who are—often do not feel that
they have either the skills or the access to training needed to adequately
support researchers with this work (Tang & Hu, 2019; Tenopir
et al., 2013, 2014). Academic libraries have also reported difficulties in
finding new staff with the appropriate mix of skills to support RDM (Cox et
al., 2017).
In 2019, Cox et al. suggested that future research
into how academic libraries support RDM “will help us understand the new
data-oriented skills, knowledge and attitudes professionals will increasingly
need and how LIS [library and information studies] curricula can be transformed
to provide them” (p. 1454). However, there has been little research as to what
LIS programs are currently offering their students in the way of RDM and data
librarianship-related training, which makes it difficult both to know what
their graduates could bring to RDM and data-related positions, and to understand
how their curricula could or should be updated to meet academic libraries’
needs. This qualitative study seeks to fill that gap by contributing novel
insights from interviews with representatives of LIS programs across Canada.
Aims
This study aims to contribute a qualitative analysis
of the perspectives of LIS program representatives on providing RDM and data
librarianship training opportunities to their students, beyond the information
which is available on their public-facing websites. It is organized around the
following research question:
RQ: Are Canadian
universities that offer Master-level library and information studies programs
providing their students with training in areas relating to RDM, and if so, in
what ways?
The primary objectives of the study are to determine
which programs currently provide training opportunities for students in RDM and
related areas, as well as whether programs have provided such opportunities in
the past or intend to do so in the future. In addition, we hoped to gain
insights into the factors affecting whether and how the programs offered this
type of training.
Literature Review
To date, most of the literature on RDM and
data-related training for librarians has focused on continuing professional
development/education for practicing librarians. These efforts are primarily
in-person workshops, ranging from one hour to several days in length (Conrad et
al., 2017; Cox et al., 2014; Johnson & Bresnahan, 2015; O’Malley, 2014; Southall & Scutt, 2017);
online modules (Read et al., 2019; Shipman & Tang, 2019) and hands-on
“training grounds” (Davis & Cross, 2015) have also been used. Some
opportunities take a “train-the-trainer" model, where librarians learn the
material and/or useful instructional techniques so they can teach others
(Conrad et al., 2017; Read et al., 2019; Tayler & Jafary,
2021). Other approaches are focused on community of practice-style programs,
where librarians can develop shared expertise and build confidence in a
collegial learning environment (Atwood et al., 2017; Wittenberg et al., 2018).
Training is often focused on the subject/liaison librarians who work most
closely with researchers and students (Davis & Cross, 2015; Southall & Scutt, 2017;
Wittenberg et al., 2018). Other lines of research indicate that librarians are
also participating in academic library RDM training aimed at researchers and
students (Xu, 2022; Xu et al., 2022). However, none of the training discussed
above involves LIS students.
There has been limited discussion of how LIS
programs prepare their students to become RDM or data-focused librarians, or
whether this type of training is fundamentally in scope for LIS programs. On
the one hand is research enumerating how many LIS programs offer data-related
courses, programs or certificates (Harris-Pierce & Quan Liu, 2012; Keralis, 2012; Wang & Lin, 2019) or describing
particular instances of such courses in LIS programs (Lyon, 2016). On the other
hand, there are a handful of studies which focus in part on how practicing RDM
and data-focused librarians view their LIS training in relation to their
current roles or demands for data services (Fuhr, 2019, 2022; Thomas and Urban,
2018). For example, the majority of the Canadian RDM and data librarians
interviewed by Rod (2023) felt that having an MLIS helped prepare them for
their current positions, while also acknowledging that there were not enough
data-specific courses or formal training opportunities in their programs to
give them advanced proficiency in pertinent data-related skills. In addition, a
key finding of Rod (2023) is that RDM and data librarians in Canada view
experience conducting a research project from beginning to end—including
applying for ethics approvals, securing funding, and publishing research
outputs—as critical expertise for their positions. The interview participants
of Rod’s (2023) study generally argued that their MLIS programs did not offer
enough opportunities for conducting relevant research projects. This aligns
with Thielen and Neeser’s
(2020) study on data professional job advertisements, which found that more
than half of job advertisements related to research data services positions
between 2013 to 2018 sought candidates with a second graduate degree in
addition to an MLIS. Indeed, MLIS programs are typically geared toward
professional training rather than academic research (Bright and Colón-Aguirre,
2022). Another study by Thomas and Urban (2018), which relied on a survey of
105 data librarians, found that the majority of respondents who held an MLIS
responded neutrally or negatively when asked how well their program had
prepared them for their current work, with some respondents saying that their
education was “outdated” or “too theoretical”.
Another stream in the literature focuses on the
debate regarding whether LIS programs, broadly, should adapt and incorporate
training related to major “trends” in librarianship such as scholarly
communications, RDM, data services, etc. (Kassim et
al., 2022; Lyon and Brenner, 2015; Raju, 2019). Lyon and Brenner (2015) argue
that LIS programs are shifting toward information studies broadly as a
reflection of the evolution of the field of librarianship and information
professions, rather than narrowly focusing solely on conventional training for
librarians, and that this shift necessitates updating the core curriculum of
LIS programs to include robust data-focused courses. Similarly, Fuhr (2022)
implemented a survey of academic librarians and identified a clear gap in
skills related to data services. Fuhr (2022) concludes that LIS programs should
be concerned with this gap and review their current data course offerings or
offer other formal opportunities such as internship placements with data
librarians. Alternatively, Eden (2018), a former member of the American Library
Association’s (ALA) committee on accreditation, argues that soft skills and the
core values of librarianship as a profession are foundational components of
training for MLIS programs. Missing from these discussions has been the
perspective of LIS programs, beyond what is available on websites or publicly
available syllabi.
Methods
This qualitative study relies on a semi-structured
interview methodology with five representatives of the seven anglophone
Canadian institutions offering ALA-accredited Master’s-level LIS programs
(representatives from all seven institutions were invited to participate, with
five accepting). The operationalization of an LIS program representative
includes an individual employed or appointed to respond to requests from
prospective students or who would have knowledge and the authority to comment
on the program’s curriculum. This includes individuals publicly listed, on the
program or institution’s directory or website, as the primary LIS master’s
degree program coordinator. If the primary point of contact for these programs
was unavailable or declined to participate, we invited another representative
of that institution’s program (for example, a member of the program’s
curriculum committee or the department chair). The research described in this
article was approved by the University of Calgary Research Ethics Board (File #
REB23-1180) and received external recognitions from each of the Research Ethics
Boards at each institution where a program representative was contacted to
participate in this study (Dalhousie University, McGill University, University
of Alberta, University of British Columbia, University of Ottawa, University of
Toronto, and Western University).
The 30-minute interviews were booked via Microsoft
Bookings and took place virtually via Zoom between November 21, 2023, and March
18, 2024. The interview questions focused on RDM or data-related training
opportunities in the program’s curriculum, including dedicated courses if they
were offered; activities or examples of how RDM training is incorporated
throughout the curriculum both formally and informally; and participants’
perspectives on the alignment between their program’s curriculum and the skills
and knowledge needed for data-related or RDM librarians. We neither offered a
definition nor an operationalization for RDM or data-related “training”, since
there remains disagreement within the literature regarding the sufficient or
necessary training, including content, domain knowledge, and modalities, for
data-related or RDM librarian careers. Thus, we opted to design questions that
addressed different avenues for training that have been previously identified
in the literature (e.g., curriculum/formal courses; for-credit research
projects; “on the job” training; self-guided training such as webinars or
modules; mentorship models including practicums or internships; other degrees
or educational experience in research, computer programming, or data-focused
disciplines; “train-the-trainer" professional development models; etc.).
For a full list of interview questions and the corresponding codebook, see the
deposited dataset (Abel and Rod, 2024).
The interviews were automatically transcribed using
the transcription functionality in Zoom. The text document transcripts were
checked against the audio recordings of the interviews and were corrected as
necessary; identifying information (i.e., names of individuals and
institutions) was also removed at this stage. The
interview transcripts were then coded and analyzed by both authors
independently using Taguette (Rampin
& Rampin, 2021), an open-source desktop
application. Coding qualitative or unstructured data is an approach for
performing content analysis, where independent coders iteratively review and
categorize information and compare agreement (Creswell, 1994; Krippendorff, 2018). An initial iteration of coding at the
interview level resulted in the development of a codebook of high-level
categories and themes.
After one round of coding using the codebook, the
inter-rater agreement between both coders was below the threshold for
reliability, e.g., percent agreement > 90% or κ > .70 (Krippendorff, 2004; Kurasaki,
2000; Lombard, Snyder-Duch, & Bracken, 2002, p.
596). At this stage, the codebook was reviewed and definitions for codes were
refined. Subsequent rounds of coding used the response —i.e., the content
between one question from the interviewer and the next—as the basic unit of
analysis. In the case of courses, students who had pursued research projects or
independent studies, and faculty supervisors, each discrete course and
individual was coded so that accurate lists or counts could be obtained.
After a second round of coding, the inter-rater
agreement for two of the five interviews reached an appropriate level of
reliability with the other three interviews approaching that level. After the
third round of coding, the inter-rater agreement, calculated using SPSS,
reached a robust reliability threshold with Cohen’s Kappa > .70 for each
transcript (see Table 1). At this point, the coders reviewed and resolved all
remaining disagreements, and a final set of reconciled codes are used as the
basis for data analysis. To ensure confidentiality of participants’ identities,
pseudonyms are used in describing results.
Table 1
Inter-rater Reliability
Analysis Results Following Three Rounds of Independent Coding
|
Interview |
Percent
Agreement |
Cohen's
Kappa (κ) |
|
Participant
1 (Jordan) |
90.32% |
0.85 |
|
Participant
2 (Emma) |
83.87% |
0.76 |
|
Participant
3 (Sophia) |
96.77% |
0.94 |
|
Participant
4 (Blake) |
83.87% |
0.76 |
|
Participant
5 (Noah) |
93.55% |
0.90 |
Results
Overall, all the LIS program representatives
affirmed that either formal or informal avenues of training opportunities in
RDM and data-related skills are available for students interested in these
careers or topics. In general, five major themes emerged from the interviews,
including:
· The
range of RDM and data-related courses and training opportunities currently
available in the programs;
· The
need for programs to develop a curriculum that meets the requirements of many
stakeholders;
· The
challenges posed by the limited pools of both available instructors and
interested students;
· Mentorship
and advising strategies for students interested in RDM or related career paths;
and
· The
effects on curriculum development of a rapidly changing library landscape.
Courses
First, we asked the LIS program representatives to
discuss RDM-related courses, courses containing RDM-related topics, and other
data-related courses that may be available to students. Three of the representatives
indicated that there is a full course on RDM available within their program’s
curriculum; at Blake’s school, this course is called ‘data curation’, but the
content is essentially RDM. Across the programs, RDM courses are typically
offered as an elective or special topic, although Jordan mentioned that as part
of their “current curriculum review”, their institution has “recommended that
it become a permanent course”. The representatives also indicated that there is
demand among students for the RDM course, as emphasized by Jordan who remarked
that there is “substantial” enrollment because the course “isn’t offered that
often”. Relatedly, in terms of scheduling the RDM course, three program
representatives mentioned that the course is meant to be offered every two
years, but this has varied recently due to instructor availability at one
institution, the lingering scheduling effects of the pandemic at another
institution, and the redesign of the course around a newly released
Canadian-focused RDM open educational resource (Thompson et al., 2023) at the
third institution. A final interesting finding regarding the RDM courses across
LIS programs in Canada is that two of the programs rely on their institutions’
RDM librarians to serve as the course instructors. As Emma explained, “we’ve
had to bring in someone not from [our] faculty to teach”.
When asked about other data-related courses, three
program representatives listed courses that they believed could build the
competencies and data skills needed to be competitive as a data librarian. The
courses mentioned explicitly by the representatives include data visualization,
database design, data analytics, Python programming, data science, and a course
on a specific type of open data.
Although two program representatives acknowledged
that their programs do not offer any RDM or data-related courses, all five
representatives discussed other related courses in which students have
opportunities to engage with the more general skills related to RDM and data
librarianship. A few examples of courses listed by the representatives that
could provide the necessary “learning objectives”, as described by Blake,
include digital curation and/or preservation, metadata, GIS, information
management or organization, and bibliometrics. All the representatives argued
that students can gain relevant skills and knowledge from courses that are not
necessarily focused on RDM or data per se. Jordan gave the example of “archival
courses” such as “digital preservation”, which are “not exactly on the area,
but many of the same concepts would be introduced in those courses”. Many of
the program representatives also emphasized that instructors “talk” about data
in a variety of courses or that a one-off lecture on RDM is offered, albeit
inconsistently, in some related courses such as research methods.
The last topic that emerged related to courses in
LIS programs is the option for students to use an independent study course to
pursue RDM or data-related topics. All five program representatives affirmed
that it would be possible for students to use an independent study course to
tailor their training toward data or RDM librarianship. For example, Jordan
described “a student who really wanted to go down a path of data librarianship,
so did an independent study with me and we kind of did this thing where the
student took all of the modules from an online RDM course.”
Current Curricula and Curriculum Development
Another focus of the interview questions included
prompts to elicit information about the pathways for developing skills and
knowledge in data topics within the curriculum of each LIS program, including
options beyond the courses discussed above. This resulted in two broad
sub-themes: how the current curriculum could support RDM and data-related
learning opportunities, and how curriculum development is shaped by competing
demands and capacity-related constraints.
In terms of their current curricula, when asked if
there is any formal or informal focus on data or RDM, three program representatives
mentioned that there is a formal focus or emphasis within their programs,
though these pathways focus less on RDM for academic contexts and more on data
science or interdisciplinary industry-focused data professions. However, almost
all the representatives discussed informal ways that students could focus on
data or RDM including independent study courses. For example, Blake mentioned
that they offer an independent study course “where you would actually have the
opportunity to pursue a research project on maybe data curation or what have
you.” Noah offered a similar perspective, stating that “I think you could
cobble together with our courses that we offer something that would give you
some insights”.
In addition to independent study courses, all
program representatives mentioned that an independent research project would
also be a feasible pathway for training or gaining competencies and knowledge
about a topic related to RDM or data. Three program representatives explained
that their programs incorporate a thesis or thesis-style project that could be
used to focus on RDM or data. To gauge the interest in project-based options
for RDM skills development, we asked program representatives how many students
have pursued RDM or data-related research projects in the past few years. Two
of the program representatives could name or discuss two students each, while a
third representative searched through a directory of thesis-style projects to
identify four students. The other two program representatives could not recall
whether any student had conducted an RDM or data-related research project.
In terms of the influences affecting curriculum
development, an overarching theme that emerged from this discussion is the
importance of ALA accreditation (see Committee on Accreditation, 2024) and its
related curricular review on the availability or unavailability of data or
RDM-related courses or training. Most of the program representatives discussed
recently completed, current, or impending preparations for seeking
reaccreditation, including reviewing their curricula. However, the consequences
of the curricular reviews on RDM and data-related offerings vary across
programs. For example, Jordan stated that because of their review, they have
“recommended that [the RDM course] become a permanent course”. On the other
hand, Blake described a meeting with their program’s advisory board where they
asked what these employers were looking for in program graduates:
We came to them with, among other things, the sort
of update of where we are with accreditation, like program review, and
curriculum review and so on. And one of the things we asked them was, so you
know, what do you need from our students? … And one of the things they said
was, we’re not looking for specialists. Because our employees have to be
versatile. And what we hire them for, if they’re very good at it that’s great,
but their job is going to change. So, you know, don’t produce highly
specialized students who are not versatile in their employment characteristics.
Number 2 was…we need people with people skills. With soft skills. Like in
libraries, what they were noticing, the [City 1] Public Libraries, that they’re
dealing with people problems all the time, you know, mental health, addiction,
violence…the problems in society. But what I found kind of interesting and
surprising, apropos RDM, was that because RDM is increasingly important in -
from the point of view of funding agencies, SSHRC and NSERC [two Canadian
funding agencies] and so on, then it became - like it stood out to me that RDM
was a skill that is sort of up and coming.
RDM is thus only one of many priorities Blake’s
program must consider when developing its curriculum.
Overall, program representatives offered mixed
perspectives on whether an RDM course should be offered formally within LIS
curricula. As summarized by Noah, “if we feel like there’s an argument to be
made for offering [an RDM course], then that’s definitely something we would
listen to, and we could recommend to the department”. At least half of the
program representatives expressed some skepticism that RDM is a topic general
enough for information professionals as to warrant inclusion in the core or
permanent curriculum either as a course or as a focus.
Limited Pools of
Instructors and Interested Students
The program representatives’
responses suggested that the lack of two key levers affects their RDM and
data-related course offerings: instructor availability, and students’ interest
in and readiness for those courses and/or careers.
All the program
representatives described problems finding or retaining instructors to teach
RDM and data-related courses. A reliance on adjunct instructors, as is the case
at two of the LIS programs, can affect how often the course is offered. For
example, Jordan explained that their program’s main challenge with the course
“is being able to offer it on a regular basis because [Adjunct Instructor] has
not always been available to teach it every year”. They noted as well that it
is generally difficult to find instructors working in the RDM field who are
available to teach the course.
A general lack of permanent
faculty members who were available to teach RDM or other data-related courses
was mentioned by four of the five program representatives. Several reasons for
this were provided by the representatives. Some schools simply do not have
enough faculty members, and some are experiencing budget limitations which have
affected the hiring of new faculty. Other schools have more faculty members,
but not necessarily any with RDM or data-librarianship related expertise which
could be useful in these courses. Jordan described a new hire who is a computer
scientist but does not have an LIS degree, acknowledging that “this will
probably expand our offerings related to data, but not…necessarily in the
professional context of data librarianship.” Permanent faculty may also be
unavailable to teach these types of courses due to sabbaticals, leaves, and
secondments.
Two of the representatives
from programs with RDM courses raised the issue of whether students are either
interested in or ready for—or consider themselves ready for—RDM and/or
data-related career paths. Jordan, who has taught a course on public domain
data sources, described their experience with students’ data literacy in the
most recent offering of the course:
When I’ve taught [the course], sometimes we’ve kind of almost like jumped
over the basic data literacy and started doing projects and, you know, like
here’s the data set, play with it…. Of the 18 students in that class at the
beginning of the class, I said, okay, rate yourself on data novice, data
newbie, you know, up to data expert and everybody was on the lower end of that
scale, everybody in the class. I mean, that’s a self-assessment. Some of them,
I think, have more knowledge than they realize they have. But, and then, when
we did some of these projects, you see that there are students who…are lacking
some of the fundamentals. So, part of our challenge, I think, is the extent to
which students are equipped.
Similarly, Emma’s program has
a data management class (not RDM) as one of its required courses, which they
say is not necessarily welcomed by the students:
Some students always kind of push back but our view is even when we talk to
people that are more interested in sort of the librarian side of things, data
is still an incredibly important part of everybody’s job…. We’ve kind of taken
the position that, you know, data literacy is critical to almost everybody in
society now. So, you know, this is something definitely all our students should
have.
They also mentioned
re-considering this requirement, but eventually deciding to keep it due to
working librarians’ views on data literacy:
We have a lot of core [courses] and we are trying
to streamline them and it was interesting because some students really argued
for, like, this isn’t core, but then when we had conversations with
professionals we were hearing just as much from you know public librarians,
academic librarians that data skills are essential so we just decided to stick
with it.
Both Jordan and Emma also
estimated that the number of students who would be interested in RDM or
data-related librarianship is limited. They suggested that while one-third to
one-half of their students would be interested in academic librarianship
generally, a much smaller percentage would be interested in these types of
careers. As Emma said, “it is a really exciting area and we always try and get
our students interested in it but…unless they kind of get a sense of what they
can do with it, it’s harder.”
Career Advice
Despite the challenges they
face in offering a formal RDM or data-related curriculum, all the
representatives were able to think of career advice they would offer to
students looking at careers in this area. Most emphasized that they would
advise students to be strategic in their selection of courses and to rely on
their program’s curriculum for developing the relevant knowledge and skills.
Sophia stated they would advise students to focus on tailoring their
assignments, explaining:
This is what I tell most students, like tailor your assignments because
when you go to your interview you want to be able to connect what you’ve
learned and what you did, that this was conscious decision-making. Like, I
wanted to learn about data management, so I did this for this assignment.
Program representatives also
discussed the importance of networking or building relationships within the RDM
community, external resources, and internships or practicums. Three representatives
discussed the importance of involvement in the broader community of practice of
RDM librarians in Canada. For example, two representatives specifically
mentioned the Canadian community listserv for data and RDM librarians
(CANLIB-DATA), with Jordan explaining that “the first thing I would advise and
have [them do] is to join the listserv. I can’t remember what it’s called, but
I’m on that list. I’m just a lurker there but there’s always, you know, good
topics there and that’s a good opportunity to stay in tune with what’s going
on”. Other advice related to participating in the RDM community included
attending relevant conferences to network and connecting students with alumni
in academic RDM librarianship positions. As emphasized by Jordan:
One of the features of this community, I think, is the cooperative,
collaborative professional development. So there are
resources online, you know, there are meetings they can go to where they’re
going to meet and learn from other people. And so, I would say tap into that
network.
Relatedly, two program
representatives argued that external resources, such as open online courses or
webinars, can fill in gaps in the formal LIS curriculum. Sophia also named
their institution’s libraries’ workshops as a potential resource for gaining
further training, stating that “we’re fortunate enough that at [our] libraries
our students actually get - you know, we have the open data week… So, there are
opportunities for our students to attend these sessions and get exposure at
least”.
Other categories of advice for
pursuing a career in RDM librarianship included seeking internship or practicum
opportunities, conducting an independent research project, and seeking
opportunities for mentorship by faculty members. Interestingly, one program
representative mentioned that they would advise students to do a research
project or thesis as a way to gain experience in going through the process of
managing data over the lifecycle of a project as opposed to focusing on an RDM
or data topic to gain knowledge about these fields of librarianship. As Noah
explained, “I do think they would want to understand the research process a
little bit more. So I don’t know if that would entail
actually doing research - that might be a bit much. But understanding the inner
workings of scientific research”.
Finally, one program
representative suggested that LIS programs are not necessarily the best fit for
students aiming to pursue the more technical data skills that may be needed for
data or RDM librarianship. As noted by Emma:
You know, we also try and be really thoughtful and sort of say to a
student, if you are good at using tools and doing all those things, but if you
are someone who really wants to go more, sort of, the programming route, then,
you know, we think another program is better for you. So
we try and kind of be clear about that.
The Changing
Library Landscape
All the program
representatives mentioned that as the role and importance of RDM is evolving in
the research landscape, their programs must find appropriate ways to respond to
the changes. Some suggested that RDM needs have not become pervasive as quickly
as they might have anticipated, and that other concerns may be taking their
place. Sophia said:
I’m really thinking that research data management was like - it was a big
thing. Like a few years ago. But now I’m thinking that the big thing is AI…. So this conversation is just getting me thinking about…how
do we keep the momentum going, or the visibility of data librarianship…or
research data management, and how do we keep that visible within the program?
Sophia added that a criticism
they hear about LIS programs is “we’re a bit of trend chasers…so then how do we
build in the sustainability?” The tension between responding to the growth of
RDM and that of other emerging areas was also observed by Noah:
[RDM is] getting bigger now with the open science movement. And, you know,
we’ll be exposed to it more and more. So we’ll also
need to support it more and more, it’ll become probably a bigger element of
academic librarianship. So in that case, we want to
cater to it, but at the same time, you know, there are a lot of other things
also developing really rapidly.
Noah referenced the situation
at their own institution’s library as an example of how difficult it is to
determine how many RDM librarians will be needed in the future:
[Here it’s] one person that’s kind of handling all this [RDM] stuff. And if
that gives the impression that it’s kind of a peripheral thing, then as a school
we would need to better understand how important that position is. Not just
theoretically, but actually like on the floor. We have dozens of librarians and
only one of them [does RDM].
As well, it takes time and
effort to ensure that RDM and data-related skills are taught appropriately.
Jordan stated that because the landscape is shifting so quickly, their
program’s RDM course needed to be redesigned almost every time it was offered,
which has taken time and led to delays in offering it regularly. Blake
described the process of responding to these shifts as like sailing “a long
heavy boat, you know – it’s like a tanker or something. It takes a long time to
steer it.”
Despite the uncertainty and
challenges that the changing RDM landscape might present for their programs,
two of the representatives from programs with RDM courses suggested that these
developments present excellent opportunities for students. As emphasized by
Emma:
If all researchers now have to have research data management plans and
really almost plan their research with the idea that…ideally their data should
be reused, that’s a whole new thing, so that we think that this is an area that
we actually need to spend more time and attention on, and a great opportunity
for students.
Jordan also observed that
having RDM or data-related training is “a great way to set yourself apart, like
in terms of a skill set, in a job application”, but admitted that getting
students interested in this area is not always easy, saying “part of my battle
is like this is where the new jobs are – like, you need to realize this.”
Discussion
This qualitative study aimed
to address whether Canadian universities that offer Master-level LIS programs
are providing their students with training related to RDM librarianship and, if
they are offering such training, to determine what that training consists of or
includes. A key finding of this study is that LIS program representatives at
Canadian institutions are very willing to support students in their programs
who express interest in RDM or data-related librarianship, whether through an
existing formalized focus area in the curriculum or an informal pathway
combining coursework, self-guided trainings, practicums or internships,
tailored research projects, and/or mentorships. Interestingly, although we did
not define what we meant by ‘training’ in the context of data or RDM
librarianship, none of the program representatives asked us for such a definition,
and none explained their own understanding of what such training would entail.
This may explain some of the variation across their responses.
The multiple mentions of
non-course-based learning opportunities supports recent research by Torres et
al. (2022) advocating for mandatory practicums/internships in LIS programs.
Torres et al. (2022) argue that without internship experience, “students do not
gain opportunities for becoming meaningfully connected with the realities of
the library workforce skills that are articulated in job advertisements” (p.
3). In RDM and data-related librarianship, this learning pathway may be
particularly salient. However, a couple of the program representatives
suggested that students would need to approach a program representative or
advisor and request help in curating a pathway, be it formal or informal,
toward RDM or data librarianship. In this way, in most LIS programs in Canada,
the onus remains on students to actively seek guidance for RDM or data-related
career pathways. Thus, a major gap in the literature that future research could
address is to elicit student perspectives on whether LIS programs prepare them
or offer them opportunities to prepare for careers as RDM or data-related
librarians, in addition to their level of awareness of and interest in these
types of careers.
The program representatives
also suggested that there is a tension for programs between developing
information professionals with a general and broad base of knowledge and
training specialists in more niche areas. Although the representatives
mentioned various current trends in librarianship that their programs might be
expected to respond to—e.g., AI, data literacy, open science, and similar
“21st-century competencies that meet the needs of the current job market” (Kassim et al., 2022, p. 407)—they appear somewhat hesitant
to significantly redevelop existing curricula or add courses that may be
difficult to maintain, particularly in light of the number of other demands on
their curricula. Indeed, a second key novel finding of this study is how
heavily ALA accreditation weighs on considerations on the types of courses and
content to offer or emphasize within the programs. For example, as mentioned by
Sophia and Noah, there is an impression among professors involved in curriculum
development committees in LIS programs that most jobs are generalist in nature
at public libraries, industry, or liaison roles at academic libraries. The
salience of ALA accreditation and the actual or perceived requirements involved
with that process is an aspect of LIS program curriculum development that has
not been discussed or identified in previous studies on RDM or data
librarianship skills development, which have focused more on the individual
attitudes of data and RDM librarians (e.g., Rod, 2023; Thomas and Urban, 2018).
Some representatives also
argued that it would be inefficient for a program to focus on specialized
training when they perceive there to be a lack of interest or awareness among
incoming students and very few specialized jobs in RDM or data librarianship.
The program representatives generally agreed that there is a need to be
somewhat responsive to major trends, but also expressed uncertainty about where
to direct their efforts regarding developing courses or seeking out instructors
with expertise in data-related fields or topics. The participants generally
agreed that they find it difficult to predict which trends will become
ubiquitous requirements for or expectations of librarians, and at what point it
makes sense to invest in overhauling existing curricular content, especially
regarding core course requirements.
All program representatives
discussed the challenges in meeting the demands of many stakeholders and
requirements, such as ALA accreditation and employer demands, but none of them
discussed actively working with their own institution’s academic library to
align with employment needs. Notably, only one representative mentioned that
they actively sought advice from their institution’s practicing librarians,
particularly in relation to a core course on data management rather than an
elective RDM course. Another program representative mentioned that they would
be open to their academic library making the case, as an employer, that RDM or
data-related competencies will be required of academic librarians more
generally, and that this would thus necessitate the offering of relevant
courses within the LIS programs. This finding is consistent with Freeberg and Vera’s (2021) recent study, which found that
practicing librarians also perceive a disconnect between LIS programs and
employer expectations of practitioners. Participants in this study also
highlighted the need for LIS programs to engage more robustly with
practitioners when planning courses and developing curriculum. Overall, the key
implication of this finding is that communication and engagement between
academic libraries and their LIS program counterparts should be improved or
formalized in curriculum development. In addition, LIS programs should engage
broadly with academic library employers, including smaller institutions that
may not have their own LIS programs, but where liaison librarians may be more
likely to “wear many hats” and support services such as RDM as part of their
role (e.g., Rod, 2023). This would provide a more holistic perspective of the
demand for RDM or data-related skills in academic librarianship as compared to
measuring demand in terms of full-time equivalent (FTE) dedicated roles.
Another consistent topic
discussed by most program representatives included the challenges of offering
data or RDM-focused courses when the landscape of RDM best practices and work
is constantly evolving. In this way, this study contributes a direct response
from LIS programs reinforcing consistent findings across several recent studies
that have demonstrated or identified a gap between LIS program curricula in
different countries and the employability or professional readiness of
graduates in both generalist and specialist roles (Kassim
et al., 2022; Raju, 2019; Torres et al., 2022). For example, Raju (2019)
compared 196 job descriptions for scholarly communications librarians, which
was operationalized in the study as including RDM and data librarianship, to
LIS course descriptions in South African programs, concluding that “LIS school
curricula in South Africa need to do more to respond to new and emerging
scholarly communication competencies required in the professional workplace.
This appears to be part of a global trend” (p. 22). One way to address this gap
could be for LIS programs to reevaluate the formal curricular tracks related to
academic librarianship. This perspective is supported by Colón-Aguirre and
Bright’s (2024) study that found, via interviews with liaison librarians, that
there is a set of several courses (e.g., reference, instruction, collection
development) that could be combined as a track for students aiming to pursue
academic librarianship. Similarly, functional areas of academic librarianship,
including RDM and data services, could be packaged as part of required courses
for students interested in these areas.
One limitation of this study
is that it is focused on anglophone Canadian LIS programs, of which there are
only seven, and thus may not reflect the curricula or state of LIS programs
across other countries. We did not include the single francophone LIS program
in Canada (at the Université de Montréal) as neither author of this study could
conduct the interview fluently in French. In addition, only five representatives
responded to requests for interviews. For the two additional institutions, we
reached out to all potential representatives we could identify from their
public-facing websites who met the inclusion criteria for this study and did
not receive any response. Although five interviews could be considered a small
sample, this represents a majority of the LIS programs in Canada, and programs
in five different provinces are represented in the sample. In addition, this
study is qualitative by design, which is not inherently intended to maximize
representativeness, but rather to surface insights that would not be easily
uncovered using other methodological approaches. Future research could
replicate this methodological approach and conduct qualitative interviews with
LIS program representatives across a variety of countries and linguistic
contexts.
Conclusion
This qualitative study sheds light on both the
support that Canadian LIS programs can provide to students who are interested
in RDM and data-related careers in academic libraries, and the challenges those
programs face in providing that support. In general, LIS program
representatives expressed enthusiasm for helping students carve career pathways
in RDM or data-related librarianship.
Overall, five major themes emerged from the
interviews, including the range of RDM and data-related courses and training
opportunities currently available in the programs; the ways in which the
representatives would mentor and advise students interested in RDM or related
career paths; the challenges posed by both the lack of instructors for RDM and
data-related courses, and the lack of students who are interested in, or ready
to pursue, data-related careers; the need for programs to develop a curriculum
that meets the requirements of many stakeholders; and the effects of the
rapidly changing library landscape on LIS curriculum development. The balancing
act the programs face was encapsulated in one representative’s statement: “we
cannot cover everything, essentially”. Despite these challenges, LIS programs
continue to try to meet the needs of their diverse student population, in
addition to the needs of a wide range of employers and the requirements of the
ALA accreditation process, although they cannot always keep up with the rapid
changes and evolving trends that are the hallmark of modern librarianship.
The views of LIS program representatives have not
previously surfaced in the discussion around the training of RDM and data
librarians, which has largely been focused on practicing librarians and their
academic library employers. This study thus provides not only a novel
contribution to the literature but also practical insights that may lead to
improved training and development of the next generation of RDM and data
librarians. A potential future line of inquiry should be to determine how
current LIS students feel about these types of careers and the training they
feel they need to successfully take on those roles in the future.
Author Contributions
Jennifer Abel: Conceptualization
(lead), Data curation (lead), Investigation (equal), Methodology (equal),
Formal analysis (equal), Validation (supporting), Project administration
(lead), Writing – original draft (equal), Writing – review & editing
(equal) Alisa B. Rod: Conceptualization (supporting), Data curation
(supporting), Investigation (equal), Methodology (equal), Formal analysis
(equal), Validation (lead), Project administration (supporting), Writing –
original draft (equal), Writing – review & editing (equal)
References
Abel, J., & Rod, A.B. (2024). Documentation for:
LIS program representatives’ perspectives on preparing students for careers in
research data management and data-related librarianship [Dataset].
Borealis. https://doi.org/10.5683/SP3/J80UA5
Andrikopoulou, A., Rowley, J., &
Walton, G. (2022). Research data management (RDM) and the evolving identity of
academic libraries and librarians: A literature review. New Review of
Academic Librarianship, 28(4), Article 4. https://doi.org/10.1080/13614533.2021.1964549
Atwood,
T. P., Condon, P. B., Goldman, J., Hohenstein, T., Mills, C. V., & Painter,
Z. W. (2017). Grassroots professional development via the New England Research
Data Management Roundtables. Journal of eScience Librarianship, 6(2),
Article 2. https://doi.org/10.7191/jeslib.2017.1111
Bright, K. M., & Colón-Aguirre, M. (2022). Prepare to
be unprepared? LIS curriculum and academic liaison preparation. The Journal
of Academic Librarianship, 48(6), Article 102602. https://doi.org/10.1016/j.acalib.2022.102602
Colón-Aguirre, M., & Bright, K. M. (2024). “So, That
Would Have Been Useful”: Curriculum in LIS in Support of Liaison Librarian
Preparation. Journal of Education for Library and Information Science.
Advance online publication. https://doi.org/10.3138/jelis-2023-0022
Committee
on Accreditation of the American Library Association. (2024). Accreditation
process, policies and procedures (AP3) (5th ed.). Retrieved July 17, 2024,
from https://www.ala.org/sites/default/files/2024-06/AP3_all_sections_5th_edition_COA%20approved%20041924%28UpdatedLinks%29.docx
Conrad, S., Shorish, Y.,
Whitmire, A. L., & Hswe, P. (2017). Building
professional development opportunities in data services for academic
librarians. IFLA Journal, 43(1), 65–80. https://doi.org/10.1177/0340035216678237
Corrall, S. (2012). Roles and responsibilities: Libraries,
librarians and data. In G. Pryor (Ed.), Managing Research Data. (pp.
105–134). Facet. https://doi.org/10.29085/9781856048910.007
Corrall, S., Kennan, M. A., &
Afzal, W. (2013). Bibliometrics and research data management services: Emerging
trends in library support for research. Library Trends, 61(3),
636–674. https://doi.org/10.1353/lib.2013.0005
Cox, A., Verbaan, E., &
Sen, B. (2014). A spider, an octopus, or an animal just coming into existence?
Designing a curriculum for librarians to support research data management. Journal
of eScience Librarianship, 3(1), Article 1. https://doi.org/10.7191/jeslib.2014.1055
Cox,
A. M., Kennan, M. A., Lyon, L., & Pinfield, S.
(2017). Developments in research data management in academic libraries: Towards
an understanding of research data service maturity. Journal of the
Association for Information Science and Technology, 68(9),
2182–2200. https://doi.org/10.1002/asi.23781
Cox,
A. M., Kennan, M. A., Lyon, L., Pinfield, S., & Sbaffi, L. (2019). Maturing research data services and the
transformation of academic libraries. Journal of Documentation, 75(6),
1432–1462. https://doi.org/10.1108/JD-12-2018-0211
Creswell,
J. W. (1994). Research design: Qualitative and quantitative approaches.
Sage.
Davis,
H. M., & Cross, W. M. (2015). Using a data management plan review service
as a training ground for librarians. Journal of Librarianship and Scholarly
Communication, 3(2), Article 2. https://doi.org/10.7710/2162-3309.1243
Eden,
B. L. (2018). The relevance of ALA accreditation: An insider’s view of the ALA
committee on accreditation. In J. Percell, L.C.
Sarin, P.T. Jaeger & J.C. Bertot (Eds.), Re-envisioning
the MLS: Perspectives on the future of library and information science
education (Advances in Librarianship, Vol.44A). (pp. 45-56). Emerald
Publishing Limited.
Federer,
L. (2018). Defining data librarianship: a survey of competencies, skills, and
training. Journal of the Medical Library Association, 106(3),
294–303. https://doi.org/10.5195/jmla.2018.306
Fuhr,
J. (2019). “How do I do that?” A literature review of research data management
skill gaps of Canadian health sciences information professionals. Journal
of the Canadian Health Libraries
Association/Journal de l'Association des bibliothèques de la santé du Canada, 40(2), 51–60. https://doi.org/10.29173/jchla29371
Fuhr,
J. (2022). Developing data services skills in academic libraries. College
& Research Libraries, 83(3), 474–502. https://doi.org/10.5860/crl.83.3.474
Government
of Canada. (2021). Tri-Agency research data management policy. Retrieved
August 15, 2024, from https://www.science.gc.ca/eic/site/063.nsf/eng/h_97610.html
Harris‐Pierce, R. L., & Quan Liu, Y. (2012). Is data curation education at library and information science
schools in North America adequate? New Library World, 113(11/12),
598–613. https://doi.org/10.1108/03074801211282957
Johnson,
A. M., & Bresnahan, M. M. (2015). DataDay!: Designing and assessing a research data workshop for
subject librarians. Journal of Librarianship and Scholarly Communication,
3(2), Article 2. https://doi.org/10.7710/2162-3309.1229
Kassim, M., Katunzi-Mollel,
K., & Mwantimwa, K. (2023). Assessing library and
information science graduates’ skills and knowledge against 21st-century
employability demands. IFLA journal, 49(2), 407–418. https://doi.org/10.1177/03400352221118695
Keralis, S. D. C. (2012). Data curation education: A snapshot.
In L. Jahnke, A. Asher & S. D. C. Keralis. The
problem of data (pp. 32–43). Council on Library and Information Resources.
Retrieved July 30, 2024, from http://www.clir.org/pubs/reports/pub154/pub154.pdf
Khair, S., Dara, R., Haigh, S.,
Leggott, M., Milligan, I., Moon, J., Payne, K., Portales-Casamar,
E., Roquet, G., & Wilson, L. (2020). The current state of research data
management in Canada: An update to the LCDRI data management position paper.
New Digital Research Infrastructure Organization (NDRIO). https://alliancecan.ca/sites/default/files/2022-03/rdm_current_state_report-1_1.pdf
Koltay, T. (2016). Facing the
challenge of data-intensive research: Research data services and data literacy
in academic libraries. In D. Baker & W. Evans (Eds.), Advances in
Library Administration and Organization (Advances in Library Administration and
Organization, Vol. 35). (pp. 45–61). Emerald Group Publishing Limited. https://doi.org/10.1108/S0732-067120160000035008
Krippendorff, K. (2004). Reliability in
content analysis: Some common misconceptions and recommendations. Human
communication research, 30(3), 411–433. https://doi.org/10.1111/j.1468-2958.2004.tb00738.x
Krippendorff, K. (2018). Content
analysis: An introduction to its methodology. Sage.
Kurasaki, K. S. (2000). Intercoder
reliability for validating conclusions drawn from open-ended interview data. Field
Methods, 12(3), 179–194. https://doi.org/10.1177/1525822X0001200301
Lombard, M., Snyder‐Duch, J.,
& Bracken, C. C. (2002). Content analysis in mass communication: Assessment
and reporting of intercoder reliability. Human communication research, 28(4),
587–604.
Lyon,
L. (2016). Librarians in the lab: Toward radically re-engineering data curation
services at the research coalface. New Review of Academic Librarianship,
22(4), 391–409. https://doi.org/10.1080/13614533.2016.1159969
Lyon,
L., & Brenner, A. (2015). Bridging the data talent gap: Positioning the iSchool as an agent for change. International journal of
digital curation, 10(1), 111–122. https://doi.org/10.2218/ijdc.v10i1.349
National Institutes of Health. (2020). Final NIH
policy for data management and sharing. https://grants.nih.gov/grants/guide/notice-files/NOT-OD-21-013.html
O’Malley,
D. L. (2014). Gaining traction in research data management support: A case
study. Journal of eScience Librarianship, 3(1), Article 1. https://doi.org/10.7191/jeslib.2014.1059
Perrier,
L., Blondal, E., & MacDonald, H. (2018). Exploring the experiences of
academic libraries with research data management: A meta-ethnographic analysis
of qualitative studies. Library & Information Science Research, 40(3),
173–183. https://doi.org/10.1016/j.lisr.2018.08.002
Pinfield, S., Cox, A. M., &
Smith, J. (2014). Research data management and libraries: Relationships,
activities, drivers and influences. PLoS ONE, 9(12),
Article e114734. https://doi.org/10.1371/journal.pone.0114734
Raju, J. (2019). Embracing
new trends in scholarly communication: From competency requirements in the
workplace to LIS curriculum presence. Journal of Librarianship and Scholarly
Communication, 7(1), Article eP2291. https://doi.org/10.7710/2162-3309.2291
Rampin, R., & Rampin, V. (2021). Taguette:
open-source qualitative data analysis. Journal of Open
Source Software, 6(68), Article 3522. https://doi.org/10.21105/joss.03522
Read,
K. B., Larson, C., Gillespie, C., Oh, S. Y., & Surkis,
A. (2019). A two-tiered curriculum to improve data management practices for
researchers. PLoS ONE, 14(5),
Article 5. https://doi.org/10.1371/journal.pone.0215509
Rod,
A. B. (2023). It Takes a Researcher to Know a Researcher: Academic Librarian
Perspectives Regarding Skills and Training for Research Data Support in Canada.
Evidence Based Library and Information
Practice, 18(2), 44–58. https://doi.org/10.18438/eblip30297
Shipman,
J. P., & Tang, R. (2019). The collaborative creation of a Research Data
Management Librarian Academy (RDMLA). Information Services & Use, 39(3),
Article 3. https://doi.org/10.3233/ISU-190050
Southall, J., & Scutt, C. (2017). Training for research data management at
the Bodleian Libraries: National contexts and local implementation for
researchers and librarians. New Review of Academic Librarianship,
23(2–3), 303–322. https://doi.org/10.1080/13614533.2017.1318766
Steeleworthy, M. (2014). Research Data Management and the Canadian
Academic Library: An Organizational Consideration of Data Management and Data
Stewardship. Partnership: The Canadian Journal of Library and Information
Practice and Research, 9(1), Article 1. https://doi.org/10.21083/partnership.v9i1.2990
Tang,
R., & Hu, Z. (2019). Providing research data management (RDM) services in
libraries: Preparedness, roles, challenges, and training for RDM practice. Data
and Information Management, 3(2), 84–101. https://doi.org/10.2478/dim-2019-0009
Tayler,
F., & Jafary, M. (2021). Shifting horizons: A
literature review of research data management train-the-trainer models for
library and campus-wide research support staff in Canadian institutions. Evidence
Based Library and Information Practice, 16(1), Article 1. https://doi.org/10.18438/eblip29814
Tenopir, C., Sandusky, R. J.,
Allard, S., & Birch, B. (2013). Academic librarians and research data
services: Preparation and attitudes. IFLA Journal, 39(1), 70–78. https://doi.org/10.1177/0340035212473089
Tenopir, C., Sandusky, R. J.,
Allard, S., & Birch, B. (2014). Research data management services in
academic research libraries and perceptions of librarians. Library &
Information Science Research, 36(2), 84–90. https://doi.org/10.1016/j.lisr.2013.11.003
Tenopir, C., Hughes, D., Allard,
S., Frame, M., Birch, B., Baird, L., Sandusky, R., Langseth,
M., & Lundeen, A. (2015). Research data services in academic libraries:
Data intensive roles for the future? Journal of eScience Librarianship, 4(2),
Article 2. https://doi.org/10.7191/jeslib.2015.1085
Thielen, J., & Neeser, A. (2020). Making job postings more equitable:
Evidence based recommendations from an analysis of data professionals job
postings between 2013-2018. Evidence Based Library and Information Practice,
15(3), 103–156. https://doi.org/10.18438/eblip29674
Thomas,
C., & Urban, R. (2018). What do data librarians think of the MLIS?
Professionals’ perceptions of knowledge transfer, trends, and challenges. College
& Research Libraries, 79(3), Article 3. https://doi.org/10.5860/crl.79.3.401
Thompson,
K., Hill, E., Carlisle-Johnston, E., Dennie, D.,
& Fortin, E. Eds. (2023). Research data management in the Canadian
context: A guide for practitioners and learners. University of Western
Ontario: Western Libraries. https://doi.org/10.5206/YWOY9668
Wang,
Y.-Y., & Lin, C.-S. (2019). A survey of data science programs and courses
in the iSchools. Proceedings of the Association
for Information Science and Technology, 56(1), Article 1. https://doi.org/10.1002/pra2.184
Wittenberg,
J., Sackmann, A., & Jaffe, R. (2018). Situating
expertise in practice: Domain-based data management training for liaison
librarians. The Journal of Academic Librarianship, 44(3), Article 3. https://doi.org/10.1016/j.acalib.2018.04.004
Xu,
Z. (2022). Research data management training in academic libraries: A scoping
review. Journal of Librarianship and Scholarly Communication, 10(1),
Article 1. https://doi.org/10.31274/jlsc.13700
Xu,
Z., Zhou, X., Kogut, A., & Watts, J. (2022). A
scoping review: Synthesizing evidence on data management instruction in
academic libraries. The Journal of Academic Librarianship, 48(3),
Article 102508. https://doi.org/10.1016/j.acalib.2022.102508